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Computer Aided Diagnosis Sensors
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Sensors used to diagnose, monitor or treat diseases in the medical domain are known as medical sensors. There are several types of medical sensors that can be utilized for various applications, such as temperature probes, force sensors, pressure sensors, oximeters, electrocardiogram sensors that measure the electrical activity of the heart, heart rate sensors, electroencephalogram sensors that measure the electrical activity of the brain, electromyogram sensors that record electrical activity produced by skeletal muscles, and respiration rate sensors that count how many times the chest rises in a minute. The output of these sensors used to be interpreted by humans, which was time consuming and tedious; however, such interpretations became easy with advances in artificial intelligence (AI) techniques and the integration of the sensor outputs into computer-aided diagnostic (CAD) systems. This reprint presents some of the state-of-the-art AI approaches that are used to diagnose different diseases and disorders based on the data collected from different medical sensors. The ultimate goal is to develop comprehensive and automated computer-aided diagnosis by focusing on the different machine learning algorithms that can be used for this purpose as well as novel applications in the medical field.
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Keywords
- ABIDE-II
- ADC maps
- Affective Computing
- AlexNet
- Alzheimer’s disease
- artificial intelligence
- artificial intelligence regression
- ASD
- Autism
- autism spectrum disorder (ASD)
- big healthcare data
- biomedical informatics
- BITalino
- bladder monitoring
- Bland–Altman method
- blood flow velocity quantification
- bone mass
- brain imaging
- BrainAmp
- CAD system
- Cancer
- Cardiovascular Disease
- carotid intima-media thickness
- cca
- CE-CT
- cervical cancer
- channel data
- Chest
- chest X-ray
- chewing
- Classification
- CNN
- computational pathology
- Computed tomography (CT)
- computer-aided diagnosis
- conjunctival microvessel
- convolutional neural network
- convolutional neural network (CNN)
- convolutional neural networks
- convolutional neural networks (CNN)
- COVID-19
- CWT
- data association
- dataset
- Decision-making
- deep learning
- Dendritic Cells
- DEXA
- diabetic retinopathy
- diabetic retinopathy (DR)
- diabetic retinopathy classification
- diabetic retinopathy lesions localization
- Diagnosis
- digital image analysis
- discrete wavelet decomposition
- disease detection
- DTI
- DWI
- ECG
- electrical characterization
- electrocardiogram (ECG)
- emotion recognition system
- encoder-decoder model
- feature selection
- functional features
- functionality
- fundus images
- grade groups
- gradient boosting
- Healthcare
- Heart rate variability
- heart sound detection
- histopathology images
- human papillomavirus (HPV)
- ICC
- Image
- image classification
- image encryption
- Image processing
- Immune system
- IMT
- intraclass correlation coefficient
- left ventricular assist devices
- lesions detection
- low pass filter
- Lung
- lung sound detection
- Machine learning
- machine learning (ML)
- machine-learning
- Macrophages
- medicine
- Melanoma
- Mild Cognitive Impairment
- model fusion
- Morphology
- motion correction
- MRI
- multi-features
- multichannel system
- Multiple Object Tracking
- n/a
- naïve Bayes
- NC protein
- neuroimaging
- non-contact spirometry
- Nosema disease
- number of chews
- OCT segmentation
- optical coherence tomography angiography (OCTA)
- optical detection
- optical imaging system
- osteopenia
- osteoporosis
- personalized diagnosis
- physiological perfusion
- POCUS
- POUR
- PPG
- prostate cancer
- protein–protein interactions
- PSA
- pump independent
- radiomics
- RBD
- RC-CAD
- renal cell carcinoma
- respiration rate mobile application
- respiration signal
- SARS-CoV-2
- security analysis
- segmentation
- semantic attribute
- sensor-based control
- shape features
- skin cancer
- skin lesions
- smart devices
- smart wearables
- Smartphones
- sMRI
- strength training
- suction index
- suction prevention
- Support Vector Machine (SVM)
- Texture
- texture analysis
- thermal camera
- thyroid
- transfer learning
- u-net
- uveitis grading
- vessel segmentation
- VGGNet
- whale optimization
- YOLO
Links
DOI: 10.3390/books978-3-0365-9533-7Editions